def get_minibatch(self, train=True, Aug=config.Augment): if train: multiple_batches = self.train_dataset else: multiple_batches = self.val_dataset if Aug: for image_batch, seg_batch in multiple_batches: yield jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy()) else: for image_batch, seg_batch in multiple_batches: randomVariable = random() if randomVariable > 0.5 or not train: yield tf.cast(image_batch, tf.float32).numpy(), tf.cast(seg_batch, tf.float32).numpy() else: yield jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy())
def get_minibatch(self, train=True): if train: multiple_batches = self.train_dataset else: multiple_batches = self.val_dataset for image_batch, seg_batch in multiple_batches: yield jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy())
def get_minibatch(self, train=True, Aug=True): if train: multiple_batches = self.train_dataset else: multiple_batches = self.val_dataset if Aug: for image_batch, seg_batch in multiple_batches: yield jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy()) else: for image_batch, seg_batch in multiple_batches: yield tf.cast(image_batch, tf.float32).numpy(), tf.cast(seg_batch, tf.float32).numpy()
def get_one_batch(self): for image_batch, seg_batch in self.train_dataset: return jitter_image(train_batch=image_batch.numpy(), train_seg_batch=seg_batch.numpy())